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The information technologies are spreading in the modern world and are one or more interconnected software products for a particular computer that helps the user achieve this aim. Every day we receive a wealth of digital information, such as emails, articles on the Internet, messages in various communication systems, and more. Therefore, the issue of qualitative and rapid verification of its authenticity, namely, elements such as digital images, is very timeliness. Digital images are often not original. They have different types of integrity violations, such as cloning or collage. After applying these operations, the different filters for blurring, brightness and contrast variation, sharpening, etc. often are used to hide the digital image processing .These operations are performed by graphic editors such as GIMP. In this graphic editor, which is a free analogue to the Adobe Photoshop graphic editor, sharpening is implemented as an Unsharp Mask Filter. A review of open source literature has shown that little attention is paid to detecting artificial sharpening. From open sources it is known about the method for detection of artificial sharpening based on the analysis of close color pairs of the image matrix. Its main disadvantage is the high number of the type I errors and the type II errors. This fact leads to the development of new methods and algorithms for detection this type of image processing. The object of this work is to detect artificial sharpening of a digital image as an integrity violation. A study is conducted in which the formal parameters of the digital image matrix were revealed, indicating the presence of artificial sharpening. A quantitative estimation of the qualitative differences between processed and raw images is obtained and used as a threshold for detecting artificially sharpened images. An algorithm for the detection of the artificial sharpening of a digital image is developed and its efficiency is estimated, According to this algorithm, the number of the type I errors is 3,8%, and the number of the type II errors is 9,8%. The development potential of this work is to improve the detection of different ways of artificial sharpening the digital image. [ABSTRACT FROM AUTHOR]